Range sensors have revolutionized computer vision in recent years, with commodity red green blue-depth/distance (RGB-D) scanners providing solutions to challenging problems such as articulated pose estimation, Simultaneous Localization and Mapping (SLAM), and object recognition. The use of 3D sensors often relies on a simplified model of the resulting depth images that is loosely coupled to the photometric principles behind the design of the scanner. Given this intermediate representation, computer vision algorithms have been deployed to understand the world and take actions based on the acquired scene information.
Significant efforts have been devoted to optimal planning of sensor deployment under resource constraints on energy, time, or computation. Sensor planning has been employed in many aspects of vision and robotics, including positioning of 3D sensors and cameras, as well as other active sensing problems, see for example. The goal is to focus sensing on the aspects of the environment or scene most relevant to a specific inference task. However, the same principles are generally not used to examine the operation of the 3D sensor itself. Therefore, there is a need in the industry to address these shortcomings.